code: def interpolate_spline(
      train_points,
      train_values,
      query_points,
      order,
      regularization_weight=0.0,
  ):
      # First, fit the spline to the observed data.
      w, v = solve_interpolation(
          train_points, train_values, order, regularization_weight
      )
      # Then, evaluate the spline at the query locations.
      query_values = apply_interpolation(
          query_points, train_points, w, v, order
      )
  
      return query_values
  
file_path: espnet/espnet/utils/spec_augment.py
imports: import random
  import torch
label_desc: Use image node_id 10 for calling a global function with example usage: interpolate_spline(train_points, train_values, query_points, order, regularization_weight) and returns: query_values